OpenRaiser/Intern-Atlas huggingface dataset:
Grounding object perception in a naive agent's sensorimotor experience
Venue: arXiv cs.RO 2016
Year: 2016

Artificial object perception usually relies on a priori defined models and feature extraction algorithms. We study how the concept of object can be grounded in the sensorimotor experience of a naive agent. Without any knowledge about itself or the world it is immersed in, the agent explores its sensorimotor space and identifies objects as consistent networks of sensorimotor transitions, independent from their context. A fundamental drive for prediction is assumed to explain the emergence of such networks from a developmental standpoint. An algorithm is proposed and tested to illustrate the approach.
Power Talk for Multibus DC MicroGrids: Creating and Optimizing Communication Channels
Venue: arXiv cs.IT 2016
Year: 2016

We study a communication framework for nonlinear multibus DC MicroGrids based on a deliberate modification of the parameters of the primary control and termed power talk. We assess the case in which the information is modulated in the deviations of reference voltages of the primary control loops and show that the outputs of the power talk communication channels can be approximated through linear combinations of the respective inputs. We show that the coefficients of the linear combinations, representing equivalent channel gains, depend on the virtual resistances of the primary control loops, implying that they can be modified such that effective received signal-to-noise ratio (SNR) is increased. On the other hand, we investigate the constraints that power talk incurs on the supplied power deviations. We show that these constraints translate into constraints on the reference voltages and virtual resistances that are imposed on all units in the system. In this regard, we develop an optimization approach to find the set of controllable virtual resistances that maximize SNR under the constraints on the supplied power deviations.
A Robust Diarization System for Measuring Dominance in Peer-Led Team Learning Groups
Venue: arXiv cs.SD 2016
Year: 2016

Peer-Led Team Learning (PLTL) is a structured learning model where a team leader is appointed to facilitate collaborative problem solving among students for Science, Technology, Engineering and Mathematics (STEM) courses. This paper presents an informed HMM-based speaker diarization system. The minimum duration of short conversationalturns and number of participating students were fed as side information to the HMM system. A modified form of Bayesian Information Criterion (BIC) was used for iterative merging and re-segmentation. Finally, we used the diarization output to compute a novel dominance score based on unsupervised acoustic analysis.
Aligning Coordinated Text Streams through Burst Information Network Construction and Decipherment
Venue: arXiv cs.CL 2016
Year: 2016

Aligning coordinated text streams from multiple sources and multiple languages has opened many new research venues on cross-lingual knowledge discovery. In this paper we aim to advance state-of-the-art by: (1). extending coarse-grained topic-level knowledge mining to fine-grained information units such as entities and events; (2). following a novel Data-to-Network-to-Knowledge (D2N2K) paradigm to construct and utilize network structures to capture and propagate reliable evidence. We introduce a novel Burst Information Network (BINet) representation that can display the most important information and illustrate the connections among bursty entities, events and keywords in the corpus. We propose an effective approach to construct and decipher BINets, incorporating novel criteria based on multi-dimensional clues from pronunciation, translation, burst, neighbor and graph topological structure. The experimental results on Chinese and English coordinated text streams show that our approach can accurately decipher the nodes with high confidence in the BINets and that the algorithm can be efficiently run in parallel, which makes it possible to apply it to huge amounts of streaming data for never-ending language and information decipherment.
Online Unsupervised Multi-view Feature Selection
Venue: arXiv cs.LG 2016
Year: 2016

In the era of big data, it is becoming common to have data with multiple modalities or coming from multiple sources, known as "multi-view data". Multi-view data are usually unlabeled and come from high-dimensional spaces (such as language vocabularies), unsupervised multi-view feature selection is crucial to many applications. However, it is nontrivial due to the following challenges. First, there are too many instances or the feature dimensionality is too large. Thus, the data may not fit in memory. How to select useful features with limited memory space? Second, how to select features from streaming data and handles the concept drift? Third, how to leverage the consistent and complementary information from different views to improve the feature selection in the situation when the data are too big or come in as streams? To the best of our knowledge, none of the previous works can solve all the challenges simultaneously. In this paper, we propose an Online unsupervised Multi-View Feature Selection, OMVFS, which deals with large-scale/streaming multi-view data in an online fashion. OMVFS embeds unsupervised feature selection into a clustering algorithm via NMF with sparse learning. It further incorporates the graph regularization to preserve the local structure information and help select discriminative features. Instead of storing all the historical data, OMVFS processes the multi-view data chunk by chunk and aggregates all the necessary information into several small matrices. By using the buffering technique, the proposed OMVFS can reduce the computational and storage cost while taking advantage of the structure information. Furthermore, OMVFS can capture the concept drifts in the data streams. Extensive experiments on four real-world datasets show the effectiveness and efficiency of the proposed OMVFS method. More importantly, OMVFS is about 100 times faster than the off-line methods.
Duality between Feature Selection and Data Clustering
Venue: arXiv cs.IT 2016
Year: 2016

The feature-selection problem is formulated from an information-theoretic perspective. We show that the problem can be efficiently solved by an extension of the recently proposed info-clustering paradigm. This reveals the fundamental duality between feature selection and data clustering,which is a consequence of the more general duality between the principal partition and the principal lattice of partitions in combinatorial optimization.
Multi-task Recurrent Model for True Multilingual Speech Recognition
Venue: arXiv cs.CL 2016
Year: 2016

Research on multilingual speech recognition remains attractive yet challenging. Recent studies focus on learning shared structures under the multi-task paradigm, in particular a feature sharing structure. This approach has been found effective to improve performance on each individual language. However, this approach is only useful when the deployed system supports just one language. In a true multilingual scenario where multiple languages are allowed, performance will be significantly reduced due to the competition among languages in the decoding space. This paper presents a multi-task recurrent model that involves a multilingual speech recognition (ASR) component and a language recognition (LR) component, and the ASR component is informed of the language information by the LR component, leading to a language-aware recognition. We tested the approach on an English-Chinese bilingual recognition task. The results show that the proposed multi-task recurrent model can improve performance of multilingual recognition systems.
Multi-label Methods for Prediction with Sequential Data
Venue: arXiv cs.LG 2016
Year: 2016

The number of methods available for classification of multi-label data has increased rapidly over recent years, yet relatively few links have been made with the related task of classification of sequential data. If labels indices are considered as time indices, the problems can often be seen as equivalent. In this paper we detect and elaborate on connections between multi-label methods and Markovian models, and study the suitability of multi-label methods for prediction in sequential data. From this study we draw upon the most suitable techniques from the area and develop two novel competitive approaches which can be applied to either kind of data. We carry out an empirical evaluation investigating performance on real-world sequential-prediction tasks: electricity demand, and route prediction. As well as showing that several popular multi-label algorithms are in fact easily applicable to sequencing tasks, our novel approaches, which benefit from a unified view of these areas, prove very competitive against established methods.
Automated Breast Lesion Segmentation in Ultrasound Images
Venue: arXiv cs.CV 2016
Year: 2016

The main objective of this project is to segment different breast ultrasound images to find out lesion area by discarding the low contrast regions as well as the inherent speckle noise. The proposed method consists of three stages (removing noise, segmentation, classification) in order to extract the correct lesion. We used normalized cuts approach to segment ultrasound images into regions of interest where we can possibly finds the lesion, and then K-means classifier is applied to decide finally the location of the lesion. For every original image, an annotated ground-truth image is given to perform comparison with the obtained experimental results, providing accurate evaluation measures.
Reducing the role of random numbers in matching algorithms for school admission
Venue: arXiv cs.GT 2016
Year: 2016

New methods for solving the college admissions problem with indifference are presented and characterised with a Monte Carlo simulation in a variety of simple scenarios. Based on a qualifier defined as the average rank, it is found that these methods are more efficient than the Boston and Deferred Acceptance algorithms. The improvement in efficiency is directly related to the reduced role of random tie-breakers. The strategy-proofness of the new methods is assessed as well.
Local Training for PLDA in Speaker Verification
Venue: arXiv cs.SD 2016
Year: 2016

PLDA is a popular normalization approach for the i-vector model, and it has delivered state-of-the-art performance in speaker verification. However, PLDA training requires a large amount of labeled development data, which is highly expensive in most cases. A possible approach to mitigate the problem is various unsupervised adaptation methods, which use unlabeled data to adapt the PLDA scattering matrices to the target domain. In this paper, we present a new `local training' approach that utilizes inaccurate but much cheaper local labels to train the PLDA model. These local labels discriminate speakers within a single conversion only, and so are much easier to obtain compared to the normal `global labels'. Our experiments show that the proposed approach can deliver significant performance improvement, particularly with limited globally-labeled data.
AP16-OL7: A Multilingual Database for Oriental Languages and A Language Recognition Baseline
Venue: arXiv cs.CL 2016
Year: 2016

We present the AP16-OL7 database which was released as the training and test data for the oriental language recognition (OLR) challenge on APSIPA 2016. Based on the database, a baseline system was constructed on the basis of the i-vector model. We report the baseline results evaluated in various metrics defined by the AP16-OLR evaluation plan and demonstrate that AP16-OL7 is a reasonable data resource for multilingual research.
A computer program for simulating time travel and a possible 'solution' for the grandfather paradox
Venue: arXiv cs.AI 2016
Year: 2016

While the possibility of time travel in physics is still debated, the explosive growth of virtual-reality simulations opens up new possibilities to rigorously explore such time travel and its consequences in the digital domain. Here we provide a computational model of time travel and a computer program that allows exploring digital time travel. In order to explain our method we formalize a simplified version of the famous grandfather paradox, show how the system can allow the participant to go back in time, try to kill their ancestors before they were born, and experience the consequences. The system has even come up with scenarios that can be considered consistent "solutions" of the grandfather paradox. We discuss the conditions for digital time travel, which indicate that it has a large number of practical applications.
Asynchronous progress design for a MPI-based PGAS one-sided communication system
Venue: arXiv cs.DC 2016
Year: 2016

Remote-memory-access models, also known as one-sided communication models, are becoming an interesting alternative to traditional two-sided communication models in the field of High Performance Computing. In this paper we extend previous work on an MPI-based, locality-aware remote-memory-access model with a asynchronous progress-engine for non-blocking communication operations. Most previous related work suggests to drive progression on communication through an additional thread within the application process. In contrast, our scheme uses an arbitrary number of dedicated processes to drive asynchronous progression. Further, we describe a prototypical library implementation of our concepts, namely DART, which is used to quantitatively evaluate our design against a MPI-3 baseline reference. The evaluation consists of micro-benchmark to measure overlap of communication and computation and a scientific application kernel to assess total performance impact on realistic use-cases. Our benchmarks shows, that our asynchronous progression scheme can overlap computation and communication efficiently and lead to substantially shorter communication cost in real applications.
YouTube-8M: A Large-Scale Video Classification Benchmark
Venue: arXiv cs.CV 2016
Year: 2016

Many recent advancements in Computer Vision are attributed to large datasets. Open-source software packages for Machine Learning and inexpensive commodity hardware have reduced the barrier of entry for exploring novel approaches at scale. It is possible to train models over millions of examples within a few days. Although large-scale datasets exist for image understanding, such as ImageNet, there are no comparable size video classification datasets. In this paper, we introduce YouTube-8M, the largest multi-label video classification dataset, composed of ~8 million videos (500K hours of video), annotated with a vocabulary of 4800 visual entities. To get the videos and their labels, we used a YouTube video annotation system, which labels videos with their main topics. While the labels are machine-generated, they have high-precision and are derived from a variety of human-based signals including metadata and query click signals. We filtered the video labels (Knowledge Graph entities) using both automated and manual curation strategies, including asking human raters if the labels are visually recognizable. Then, we decoded each video at one-frame-per-second, and used a Deep CNN pre-trained on ImageNet to extract the hidden representation immediately prior to the classification layer. Finally, we compressed the frame features and make both the features and video-level labels available for download. We trained various (modest) classification models on the dataset, evaluated them using popular evaluation metrics, and report them as baselines. Despite the size of the dataset, some of our models train to convergence in less than a day on a single machine using TensorFlow. We plan to release code for training a TensorFlow model and for computing metrics.
TMI! How Knowledge Platforms Tame the Information Overload and Advance Global Development Through Technology
Venue: arXiv cs.CY 2016
Year: 2016

Finding reliable data to inform decisions about technology for global development remains a challenge. Easily accessible "Knowledge platforms" are a way to curate and standardize information about technology for development. Three collaborators, Engineering for Change, the Global Alliance for Clean Cookstoves and the Center for Affordable Water and Sanitation Technology (CAWST) are working together to create platforms to serve the global development sector. Such platforms could be the first step in making decisions about how to solve a problem that needs a technology-based solution. They could motivate manufacturers to demonstrate compliance with quality standards, and they could encourage independent research into each product's impact. Years of development experience worldwide have yielded a set of best practices to increase the life of a project. These platforms clarify those practices to improve quality and reduce waste. As the platforms mature, mining them for data could identify trends that influence the entire technology ecosystem. Those could include decisions about performance targets, future innovation, pricing, compliance with regulations, and funding priorities.
Education Stats Made Visible: Helping School District Managers Write Better Three-Year Plans
Venue: arXiv cs.CY 2016
Year: 2016

Problem: School district leaders in California are awash in a sea of data, but are often unable to find it, query it, or relate it with other data. Districts are islands, leaving district managers able to see only their own data. A state education agency gathers and republishes districts' data, but doesn't clean it. New laws now require them to plan in public, set goals, assign resources, and project benefits for specific groups of kids. The evidence base they need remains out of reach. Solution: Our team was commissioned by two county offices of education to design a comparative methodology and data visualization environment to help district managers write these three-year plans. Ten year of data spanning finance, staffing, students, discipline, course offerings and assessments were acquired, QA'd and integrated. A Tableau development team built 50+ pages of leaderboards and scatterplots. A documentation team wrote training materials. Each district in this county received a custom workbook which framed its district's vital signs within a context of 15 districts whose students were most like their own. This presentation shares the work and our evaluation.
Connecting Data Science and Qualitative Interview Insights through Sentiment Analysis to Assess Migrants' Emotion States Post-Settlement
Venue: arXiv cs.CY 2016
Year: 2016

Large-scale survey research by social scientists offers general understandings of migrants' challenges and provides assessments of post-migration benchmarks like employment, obtention of educational credentials, and home ownership. Minimal research, however, probes the realm of emotions or "feeling states" in migration and settlement processes, and it is often approached through closed-ended survey questions that superficially assess feeling states. The evaluation of emotions in migration and settlement has been largely left to qualitative researchers using in-depth, interpretive methods like semi-structured interviewing. This approach also has major limitations, namely small sample sizes that capture limited geographic contexts, heavy time burdens analyzing data, and limits to analytic consistency given the nuances of qualitative data coding. Information about migrant emotion states, however, would be valuable to governments and NGOs to enable policy and program development tailored to migrant challenges and frustrations, and would thereby stimulate economic development through thriving migrant populations. In this paper, we present an interdisciplinary pilot project that offers a way through the methodological impasse by subjecting exhaustive qualitative interviews of migrants to sentiment analysis using the Python NLTK toolkit. We propose that data scientists can efficiently and accurately produce large-scale assessments of migrant feeling states through collaboration with social scientists.
The Quantified Community at Red Hook: Urban Sensing and Citizen Science in Low-Income Neighborhoods
Venue: arXiv cs.CY 2016
Year: 2016

The Quantified Community (QC)--a long-term neighborhood informatics research initiative--is a network of instrumented urban neighborhoods that collect, measure, and analyze data on physical and environmental conditions and human behavior to better understand how neighborhoods and the built environment affect individual and social well-being. This initiative is intended to create a data-enabled research environment to rigorously study the complex interactions in urban neighborhoods. The QC has initially launched in three very distinct areas in New York City: at Hudson Yards, a ground-up "city-within-a-city" of approximately 20 million square feet in Manhattan, in collaboration with the Related Companies; in Lower Manhattan, a mixed-use neighborhood that attracts residents, workers, and visitors, in collaboration with the Alliance for Downtown NY; and in Red Hook, Brooklyn, an economically-distressed community facing significant development and demographic changes, in partnership with the Red Hook Initiative. This paper describes our recent pilot project to deploy novel urban sensors in Red Hook to collect and analyze quality-of-life measurements at high spatial and temporal resolution. This effort is complemented by a citizen science initiative to engage local residents in the data collection and problem-solving process to drive evidenced-based community decision-making and improve local and city governance.
Mitigating Pilot Contamination Through Location-Aware Pilot Assignment in Massive MIMO Networks
Venue: arXiv cs.IT 2016
Year: 2016

We propose a novel location-aware pilot assignment scheme to mitigate pilot contamination in massive multiple-input multiple-output (MIMO) networks, where the channels are subjected to Rician fading. Our proposed scheme utilizes the location information of users as the input to conduct pilot assignment in the network. Based on the location information, we first determine the line of sight (LOS) interference between the intended signal and the interfering signal. Our analysis reveals that the LOS interference converges to zero as the number of antennas at the base station (BS) goes to infinity, whereas for finite number of antennas at the BS the LOS interference indeed depends on specific pilot allocation strategies. Following this revelation, we assign pilot sequences to all the users in the massive MIMO network such that the LOS interference is minimized for finite number of antennas at the BS. Our proposed scheme outperforms the random pilot assignment in terms of achieving a much higher uplink sum rate for reasonable values of the Rician K-factor. Moreover, we propose a new performance metric, which measures the strength of the LOS interference and demonstrate that it is a good metric to analyze the performance of pilot assignment schemes. Theoretical analysis is provided and verified through numerical simulations to support the proposed scheme.